Gemini is Google's latest marvel in the realm of Large Language Models (LLMs), but with a twist. Unlike traditional LLMs that primarily focus on text, Gemini is designed to be multimodal. This means it can understand, interpret, and generate not just textual content but also images, audio, and video. The Gemini family comprises three sizes — Ultra, Pro, and Nano — each tailored for different levels of complexity and application scenarios, from solving intricate reasoning tasks to operating within the memory constraints of mobile devices.
The power of Gemini lies in its cross-modal reasoning capabilities. For example, it can analyze a physics problem described in a handwritten note, understand the concept, and provide a solution in mathematical notation. This level of understanding opens up new avenues for applications in education, creative content generation, and beyond, making technology more accessible and versatile.
Gemini vs. Claude vs. OpenAI
When comparing Gemini to other LLMs like Anthropic's Claude and models from OpenAI, several key differences and similarities emerge. All these models aim to advance AI's ability to understand and generate human-like text, but their approaches and capabilities in handling multimodal content set them apart. The following a summary of some benchmarks comparing Gemini family of models against other commercial and open source models.
Figure 1: Gemini performance comparisons
Reference: https://arxiv.org/pdf/2312.11805.pdf
Summary results
I ran some tests earlier today to see how Google Gemini's Pro model would perform. Speed and reliability are crucial when integrating AI into products, latency and connection timeouts can be a major stumbling block when trying to ship new features. During this test, I subjected Gemini Pro to a series of increasing concurrent requests, starting from a single one up to forty, to observe the average response time and token generation speed. The results were encouraging; Gemini Pro maintained steady performance, even as the demands grew. At the peak of forty concurrent requests, there was an expected increase in response time, but it remained responsive. These promising findings suggest that Gemini Pro is a viable option for those considering its integration. Moreover, for those requiring higher limits, Google offers the flexibility to enhance throughput capabilities through partnership agreements, providing a scalable solution for evolving project needs.
Summary of Performance
Duration: 1m
Model: Gemini-Pro
Token Input: 1700 tokens
Token Output: 300 tokens
| Concurrent | Requests | Avg Response | Avg Tokens/Sec |
|---|---|---|---|
| 1 | 10 | 5.74 sec | 52.26 |
| 2 | 20 | 5.56 sec | 53.95 |
| 4 | 44 | 5.47 sec | 53.95 |
| 8 | 83 | 5.53 sec | 54.24 |
| 16 | 173 | 5.50 sec | 54.24 |
| 32 | 166 | 10.56 sec | 28.40 |
| 40 | 162 | 12.83 sec | 23.38 |
If you have any questions or comments about the tests feel free to share them below.
Thanks
Shannon
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